AI Engineer

TEKsystems•Round Rock, TX

2d•$75 - $75•Onsite

About The Position

We are seeking an AI Engineer with demonstrated experience delivering AI-based solutions from concept through production. The role requires a high degree of independence: translating business and stakeholder requirements into secure, enterprise-aligned solutions, and carrying them through design, implementation, deployment, and adoption. The successful candidate will combine strong engineering fundamentals with current expertise in applied AI and agentic systems and will contribute to the broader team's capability through knowledge sharing and enablement.

Requirements

Demonstrated experience developing and deploying AI-based solutions in production environments, with measurable business or operational impact
Strong programming proficiency (e.g., Python, TypeScript/JavaScript, Go, or similar) and adherence to software engineering best practices, including testing, code quality, and maintainability
Hands-on experience with modern AI/LLM development, including: Context engineering - designing what informs the model's context window, including agentic retrieval and search, memory architectures, grounding in enterprise data, and structured outputs; Agentic system design - agent loop engineering, multi-agent and sub-agent orchestration, and tool/function calling; Context window management and token budgeting, including cost and latency optimization for production workloads; Evaluation of AI system quality, reliability, and safety
Solid understanding of software architecture and systems design, including API design, event-driven patterns, and data modeling for scalability and extensibility
Experience developing and/or deploying applications with large-scale impact (broad user base, high transaction volume, or organization-wide adoption)
Experience integrating with multiple systems and platforms (REST/GraphQL APIs, CI/CD pipelines, cloud services, enterprise tooling)
Demonstrated ability to work independently across the full delivery lifecycle - requirements analysis, solution design, implementation, deployment, and stakeholder engagement - with accountability for results
Strong communication and collaboration skills, with the ability to convey technical concepts to both engineering and business audiences
Working knowledge of secure development practices and experience designing solutions that meet enterprise security and compliance requirements

Nice To Haves

Experience applying AI within a security domain - application security, DevSecOps, code analysis, threat modeling, firmware security or software supply-chain security
Familiarity with secure-by-design / secure-by-default principles and relevant frameworks (e.g., OWASP, including the OWASP Top 10 for LLM Applications; NIST SSDF)
Experience with MCP (Model Context Protocol), building agent skills and tools, or extending AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor, Devin)
Experience with AI evaluation frameworks, guardrails, prompt/response caching strategies, and LLMOps in production
Experience mentoring engineers or leading technical enablement initiatives
Bachelor's or master's degree in computer science or a related field, or equivalent practical experience

Responsibilities

Design, build, and deploy AI-powered capabilities across the SDLC, including: Spec Driven Development workflows that support the translation of well-formed specifications into secure, verifiable implementations; Assurance of AI-generated code - guardrails, policy enforcement, and verification for code produced by AI assistants and agents; SDLC skills and agent tooling - developer-assist skills as well as verification skills that perform automated security checks (design review, dependency and supply-chain analysis, static/dynamic analysis orchestration, release audit support)
Integrate solutions with enterprise systems - source control, CI/CD, ticketing, security scanning, identity, and internal platforms - through APIs, webhooks, and protocols such as MCP (Model Context Protocol)
Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate trade-offs, and support solution adoption
Apply sound architecture and systems design practices: well-defined service boundaries, appropriate data models, secure defaults, observability, and extensibility
Design and operate agentic systems responsibly and efficiently, including orchestration of agent loops and sub-agents, context and token budget management, and cost/latency optimization
Evaluate emerging AI technologies and methods - agentic frameworks, tool use, agentic retrieval and memory systems, structured outputs, evaluation frameworks, LLMOps - and recommend adoption where appropriate for production use
Establish evaluation and quality practices for AI outputs, measuring accuracy, safety, and business impact, and iterating based on results
Contribute to team enablement through documentation, demonstrations, and mentoring